Invention Grant
- Patent Title: Machine learning model accuracy fairness
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Application No.: US16814603Application Date: 2020-03-10
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Publication No.: US11657323B2Publication Date: 2023-05-23
- Inventor: Manish Anand Bhide , Madhavi Katari , Ravi Chandra Chamarthy , Swapna Somineni
- Applicant: INTERNATIONAL BUSINESS MACHINES CORPORATION
- Applicant Address: US NY Armonk
- Assignee: International Business Machines Corporation
- Current Assignee: International Business Machines Corporation
- Current Assignee Address: US NY Armonk
- Agency: Conley Rose, P.C.
- Main IPC: G06F3/00
- IPC: G06F3/00 ; G06N20/00 ; G06N5/04 ; G06F3/01

Abstract:
A system includes a memory having instructions therein and at least one processor in communication with the memory. The at least one processor is configured to execute the instructions to run a machine learning base model on input data to generate base model prediction data and run a machine learning error prediction model on the input data to generate error prediction data. The at least one processor is configured to execute the instructions to generate predicted correct base model prediction data based on the base model prediction data and the error prediction data. The at least one processor is configured to execute the instructions to generate confusion values data based on the base model prediction data and the predicted correct base model prediction data. The at least one processor is also configured to execute the instructions to generate base model accuracy fairness metrics data based on the confusion values data.
Public/Granted literature
- US20210287131A1 MACHINE LEARNING MODEL ACCURACY FAIRNESS Public/Granted day:2021-09-16
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